Systematic identification of species-specific allosteric sites in bacterial glycolytic enzymes: Hexokinase, phosphoglucose isomerase, phosphoglycerate kinase, and enolase.
In this study, we developed and applied an integrative computational workflow for the systematic identification and prioritization of candidate allosteric pockets across all four glycolytic enzymes: three from Staphylococcus aureus-phosphoglucose isomerase (PGI), phosphoglycerate kinase (PGK), and enolase- and one representative hexokinase from Plasmodium vivax, included due to the absence of an experimentally determined three-dimensional structure for the S. aureus ortholog. Solvent mapping using FTMap and FTMove across oligomeric ensembles revealed multiple high-confidence cavities predominantly located at subunit interfaces, in addition to canonical catalytic sites. Independent evaluation with CavityPlus supported the presence and druggability of these pockets. Hexokinase presented 12 interface-associated pockets that emerged only upon oligomer formation and remained stable across 300 FTMove-derived conformers. PGI and PGK displayed interface- and hinge-associated cavities linked to known global motions, while the octameric enolase showed prominent central and peripheral inter-dimer pockets. Candidate pockets were subsequently evaluated using CorrSite, ESSA, PASSer, and AlloSigMA to assess features associated with allosteric communication, energetic coupling, and protein dynamics. High-confidence candidate sites were prioritized based on consensus across these complementary computational approaches. Across all four enzymes, interface-localized pockets consistently emerged as promising candidate regulatory regions, suggesting that protein-protein interfaces may represent valuable targets for allosteric modulation. Several predicted pockets, particularly in PGI and enolase, exhibited low sequence and structural similarity to the corresponding human homologs, indicating their potential for selective inhibitor design. Overall, this integrated computational framework provides a systematic strategy for identifying and prioritizing candidate allosteric pockets for future structural, biochemical, and structure-based drug discovery.